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End-to-end spoken language understanding (SLU) remains elusive even withcurrent large pretrained language models on text and speech, especially inmultilingual cases. Machine translation has been established as a powerfulpretraining objective on text as it enables the model to capture high-levelsemantics of the input utterance and associations between different languages,which is desired for speech models that work on lower-level acoustic frames.Motivated particularly by the task of cross-lingual SLU, we demonstrate thatthe task of speech translation (ST) is a good means of pretraining speechmodels for end-to-end SLU on both intra- and cross-lingual scenarios.  By introducing ST, our models reach higher performance over baselines onmonolingual and multilingual intent classification as well as spoken questionanswering using SLURP, MINDS-14, and NMSQA benchmarks. To verify theeffectiveness of our methods, we also create new benchmark datasets from bothsynthetic and real sources, for speech summarization and low-resource/zero-shottransfer from English to French or Spanish. We further show the value ofpreserving knowledge for the ST pretraining task for better downstreamperformance, possibly using Bayesian transfer regularizers.

The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation